Vitis AI Library 1.2 Release Notes - 1.2 English

Vitis AI Library User Guide (UG1354)
Document ID
UG1354
Release Date
2020-07-21
Version
1.2 English

This section contains information regarding the features and updates of the Vitis™ AI Library 1.2 release.

Key Features And Enhancements

This AI Library release includes the following key features and enhancements.

New Cloud Board Support
Alveo U50lv and U280 cards are new supported by this release.
New Model Libraries
The following new model libraries are supported.
  • face recognition
  • plate detection
  • plate recognition
  • medical segmentation
Pytorch Model Support
3 pytorch models are supported.
Add 6 Caffe Models
6 new caffe models are supported.

Changes

  1. The installation mode of the target for the edge is changed and the rpm format package is used.
  2. meta.json file in the model has been deprecated.

Compatibility

  • Vitis AI Library 1.2 has been tested with the following images.
    • xilinx-zcu102-dpu-v2020.1-v1.2.0.img.gz
    • xilinx-zcu104-dpu-v2020.1-v1.2.0.img.gz

Model Support

The following models are supported by this version of the Vitis AI Library.

Table 1. Model Supported by the AI Library
No. Neural Network ZCU102/ZCU104 U50/U50lv/U280 Application
1 inception_resnet_v2_tf Y Y Image Classification
2 inception_v1_tf Y Y
3 inception_v3_tf Y Y
4 inception_v4_2016_09_09_tf Y Y
5 mobilenet_v1_0_25_128_tf Y N/A
6 mobilenet_v1_0_5_160_tf Y N/A
7 mobilenet_v1_1_0_224_tf Y N/A
8 mobilenet_v2_1_0_224_tf Y N/A
9 mobilenet_v2_1_4_224_tf Y N/A
10 resnet_v1_101_tf Y Y
11 resnet_v1_152_tf Y Y
12 resnet_v1_50_tf Y Y
13 vgg_16_tf Y Y
14 vgg_19_tf Y Y
15 ssd_mobilenet_v1_coco_tf Y N/A Object Detection
16 ssd_mobilenet_v2_coco_tf Y N/A
17 ssd_resnet_50_fpn_coco_tf Y Y
18 yolov3_voc_tf Y Y
19 mlperf_ssd_resnet34_tf Y N/A
20 resnet50 Y Y Image Classification
21 resnet18 Y Y
22 inception_v1 Y Y
23 inception_v2 Y Y
24 inception_v3 Y Y
25 inception_v4 Y Y
26 mobilenet_v2 Y N/A
27 squeezenet Y Y
28 ssd_pedestrain_pruned_0_97 Y Y ADAS Pedestrian Detection
29 ssd_traffic_pruned_0_9 Y Y Traffic Detection
30 ssd_adas_pruned_0_95 Y Y ADAS Vehicle Detection
31 ssd_mobilenet_v2 Y N/A Object Detection
32 refinedet_pruned_0_8 Y Y
33 refinedet_pruned_0_92 Y Y
34 refinedet_pruned_0_96 Y Y
35 vpgnet_pruned_0_99 Y Y ADAS Lane Detection
36 fpn Y Y ADAS Segmentation
37 sp_net Y Y Pose Estimation
38 openpose_pruned_0_3 Y Y  
39 densebox_320_320 Y Y Face Detection
40 densebox_640_360 Y Y
41 face_landmark Y Y Face Detection and Recognition
42 reid Y Y Object tracking
43 multi_task Y Y ADAS
44 yolov3_adas_pruned_0_9 Y Y Object Detection
45 yolov3_voc Y Y
46 yolov3_bdd Y Y
47 yolov2_voc Y Y
48 yolov2_voc_pruned_0_66 Y Y
49 yolov2_voc_pruned_0_71 Y Y
50 yolov2_voc_pruned_0_77 Y Y
51 facerec_resnet20 Y Y Face Recognition
52 facerec_resnet64 Y Y
53 plate_detection Y Y Plate Recognition
54 plate_recognition Y Y
55 FPN_Res18_Medical_segmentation Y Y Medical Segmentation
56 refinedet_baseline Y Y Object Detection
57 resnet50_pt N/A Y Image Classification
58 squeezenet_pt N/A Y
59 inception_v3_pt N/A Y
  1. No1-No19 neural network models are trained based on the Tensorflow framework.
  2. No20-No56 neural network models are trained based on the Caffe framework.
  3. No57-No59 neural network models are trained based on the Pytorch framework.

Device Support

The following platforms and EVBs are supported by the Vitis AI Library1.2.

Table 2. Edge Device Support
Platform EVB Version
Zynq UltraScale+ MPSoC ZU9EG Xilinx ZCU102 V1.1
Zynq® UltraScale+™ MPSoC ZU7EV Xilinx ZCU104 V1.0
Table 3. Cloud Device Support
Accelerator Cards
Xilinx Alveo U50
Xilinx Alveo U50lv
Xilinx Alveo U280

Limitations

  • Some neural networks with mobilenet as the backbone are not supported on U50, U50lv and U280.
  • Pytorch models are not supported for edge devices.
  • Due to the limitation of Docker environment, the Multi-Task demos cannot run in DRM mode on the cloud devices.